Supplementary Materials for ‘xyz’
Haza B, Gosling CJ, Conty L, & Pinabiaux C (2022)
24/10/2022
Supplement 1. Systematic review
PRISMA flowchart
Donne moi les chiffres quand tu as fini, j’insèrerai
PRISMAstatement::prisma(found = 8002,
found_other = 7,
no_dupes = 7187,
screened = 7187,
screen_exclusions = 6913,
full_text = 274,
full_text_exclusions = 124,
qualitative = 150,
quantitative = 150,
width = 800, height = 1200)Overall N studies + participants
dat_tot## # A tibble: 1 x 4
## n_studies n_es n_nexp_out n_exp_out
## <int> <dbl> <dbl> <dbl>
## 1 144 647 8362 8045N studies (adjustment discarded)
dat.borenstein %>%
group_by(outcome_RDoC_agg) %>%
summarise(n_es = sum(trial),
n_nexp = sum(n_nexp),
n_nexp = sum(n_nexp),
nstudy1 = n(),
n_study2 = length(unique(ID_study)))## # A tibble: 4 x 5
## outcome_RDoC_agg n_es n_nexp nstudy1 n_study2
## <chr> <dbl> <dbl> <int> <int>
## 1 Mixed social processes 223 5506 90 87
## 2 Reception of facial communication 186 2168 48 45
## 3 Reception of non-facial communication 28 580 10 9
## 4 Understanding mental states 210 2670 65 60N studies (adjustment splitted)
synth_n = dat.borenstein %>%
group_by(outcome_RDoC) %>%
summarise(n_es = sum(trial),
n_exp = sum(n_exp),
n_nexp = sum(n_nexp),
nstudy1 = n(),
n_study2 = length(unique(ID_study)))
synth_n## # A tibble: 8 x 6
## outcome_RDoC n_es n_exp n_nexp nstudy1 n_stu~1
## <chr> <dbl> <dbl> <dbl> <int> <int>
## 1 Mixed social processes (adjusted) 14 277 306 8 8
## 2 Mixed social processes (non-adjusted) 209 5570 5200 82 82
## 3 Reception of facial communication (adjuste~ 18 382 511 6 6
## 4 Reception of facial communication (non-adj~ 168 1712 1657 42 42
## 5 Reception of non-facial communication (adj~ 3 141 254 2 2
## 6 Reception of non-facial communication (non~ 25 381 326 8 8
## 7 Understanding mental states (adjusted) 24 245 274 9 9
## 8 Understanding mental states (non-adjusted) 186 2521 2396 56 56
## # ... with abbreviated variable name 1: n_study2Supplement 2. ES visualization
Distribution of ES
ggplot(dat.meta, aes(x = es_adj, y = es)) +
geom_jitter(alpha = 0.3, width = 0.35, size = 2) +
geom_violin(size = 0.5, alpha = 0.5) +
theme_bw() 
Distribution of ES / outcomes
2 plots, tu choisis celui qui tu préfères
ggplot(dat.meta, aes(x = outcome_RDoC_agg, y = es)) +
geom_jitter(width = 0.05, dotsize = 0.1, alpha = 0.2, aes(fill = ID)) + geom_boxplot(trim=FALSE, alpha = 0.6) +
facet_grid(es_adj ~ "") +
theme_bw() +
labs(y = "Standardized mean difference (SMD)", x = "") +
guides(fill=FALSE) +
coord_flip()
ggplot(dat.meta, aes(x = outcome_RDoC_agg, y = es)) +
geom_violin(aes(fill = outcome_RDoC_agg), alpha = 0.3) +
geom_jitter(alpha = 0.2, width = 0.1) +
geom_boxplot(width=0.1)+
facet_grid(es_adj ~ "") +
theme_bw() +
labs(y = "Standardized mean difference (SMD)", x = "") +
theme(text = element_text(size = 18),
legend.position = "none",
axis.title.y = element_text(size=12, face="bold"),
axis.title.x = element_text(size=12, face="bold")) +
coord_flip() 
Supplement 3. Main analysis
Data analysis
V.SCE <- with(dat.meta,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
# all.equal(dat.meta$se^2, diag(unlist(V.SCE)))
SCE <- metafor::rma.mv(yi = es, V = V.SCE,
data = dat.meta,
mods = ~ outcome_RDoC - 1,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
SCE.ISQ <- metafor::rma.mv(yi = es, V = V.SCE,
data = dat.meta,
mods = ~ outcome_RDoC - 1)
het = function(x, y) {
i2 = NA
for (i in 1:nrow(vcov(x))) {
i2[i] = 100 * (vcov(x)[i,i] - vcov(y)[i,i]) / vcov(x)[i,i]
}
return(i2)
}
#
# W <- solve(V.SCE)
# X <- model.matrix(SCE)
# P <- W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
# 100 * SCE$tau2 / (SCE$tau2 + (SCE$k-SCE$p)/sum(diag(P)))
res.SCE <- data.frame(clubSandwich::coef_test(SCE, vcov = "CR2", cluster = dat.meta$ID_study)) # , cluster = df.ES$researchgroup
res.SCE.ci = data.frame(clubSandwich::conf_int(SCE, vcov = "CR2", cluster = dat.meta$ID_study))
res.SCE$target <- gsub("outcome_RDoC", "", row.names(res.SCE))
res.SCE.ci$target <- gsub("outcome_RDoC", "", row.names(res.SCE.ci))
res.SCE = dplyr::left_join(res.SCE, res.SCE.ci)
res.SCE$I2 = het(SCE, SCE.ISQ)res_main <- left_join(res.SCE, dat_RDoC)## Joining, by = "target"res_main$p_Satt_bonferroni = res_main$p_Satt * 8
res_main[,c("beta", "SE", "tstat", "df", "p_Satt",
"p_Satt_bonferroni", "CI_L", "CI_U", "I2",
"n_studies", "n_nexp_out" , "n_exp_out")] <- apply(
res_main[,c("beta", "SE", "tstat", "df", "p_Satt",
"p_Satt_bonferroni", "CI_L", "CI_U", "I2",
"n_studies", "n_nexp_out" , "n_exp_out")], 2, function(x) round(x, 3))
DT::datatable(res_main,
rownames = FALSE,
extensions = 'Buttons',
options = list( # options
buttons = c('copy', 'excel'),
scrollX = TRUE,
dom = c('ftB'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))Forest plot
res_main$SE_COR = (res_main$CI_U - res_main$CI_L) / 3.92
res_main$raw_outcome <- gsub(r"{\s*\([^\)]+\)}","",
as.character(res_main$outcome_RDoC))
tab.plot <- data.frame(
Outcome = do.call(rbind, lapply(res_main$outcome_RDoC,
function(x) paste(strwrap(x, width = 60),
collapse = "\n"))),
n_ADHD = res_main$n_exp_out,
n_nexpT = res_main$n_nexp_out,
n_studies = res_main$n_studies,
I2 = paste0(round(res_main$I2), "%"))
value_num_plot = data.frame(apply(res_main[, c("beta", "SE_COR")], 2, function(x) as.numeric(as.character(x))))
forest_modif(x = value_num_plot[ ,c("beta", "SE_COR")],
variant = "classic",
col = "Greys", xlab = "SMD", annotate_CI = TRUE,
study_table = tab.plot,
group = res_main$raw_outcome,
type = "study_only",
text_size = 3.5,
x_limit = c(-1, 2),
N = tab.plot$n_ADHD + tab.plot$n_nexpT,
x_breaks = seq(-3, 3, 1)
)
Diagnostics
# windows() ## create window to plot your file
metafor::profile.rma.mv(SCE)## Profiling tau2 = 1
## Profiling tau2 = 2
## Profiling tau2 = 3
## Profiling tau2 = 4
## Profiling tau2 = 5
## Profiling tau2 = 6
## Profiling tau2 = 7
## Profiling tau2 = 8
# dev.off()Supplement 4. Supplementary analyses
S1. Borenstein aggregated approach
meta.reg <- metafor::rma.mv(yi = es, V = se^2,
data = dat.borenstein,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
mods = ~ outcome_RDoC - 1)
res.S1.a.clean = data.frame(
beta = meta.reg$beta,
se = meta.reg$se,
ci_lo = meta.reg$ci.lb,
ci_up = meta.reg$ci.ub,
pval = meta.reg$pval,
target = gsub("outcome_RDoC", "", row.names(meta.reg$b)))
res.S1 <- left_join(res.S1.a.clean, dat_RDoC)## Joining, by = "target"DT::datatable(res.S1,
rownames = FALSE,
extensions = 'Buttons',
options = list( # options
buttons = c('copy', 'excel'),
scrollX = TRUE,
dom = c('ftB'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))S2. RVE approach
robu.main <- robumeta::robu(es ~ outcome_RDoC - 1,
data = dat.meta,
studynum = ID_study,
var.eff.size = se^2,
modelweights = "CORR",
small = TRUE,
rho = 0.8)
dat_robu_main = data.frame(
beta = robu.main$reg_table$b.r,
se = robu.main$reg_table$SE,
ci_lo = robu.main$reg_table$CI.L,
ci_up = robu.main$reg_table$CI.U,
pval = robu.main$reg_table$prob,
target = gsub("outcome_RDoC", "", row.names(meta.reg$b)),
target2 = robu.main$reg_table$labels)
DT::datatable(dat_robu_main,
rownames = FALSE,
extensions = 'Buttons',
options = list( # options
buttons = c('copy', 'excel'),
scrollX = TRUE,
dom = c('ftB'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))SCE vs RVE vs Borenstein
res.SCE.main = data.frame(
beta = res.SCE$beta,
se = res.SCE$SE,
ci_lo = res.SCE$CI_L,
ci_up = res.SCE$CI_U,
pval = res.SCE$p_Satt,
target =res.SCE$target)
res.SCE.main$Analysis = "SCE approach"
res.S1.a.clean$Analysis = "Borenstein approach"
dat_robu_main$Analysis = "RVE approach"
dat_forest_S1 = bind_rows(res.SCE.main,
res.S1.a.clean,
dat_robu_main)
meta_res_tot = meta::metagen(TE = beta, seTE = (ci_up-ci_lo)/(2*qnorm(.975)),
sm = "SMD", data = dat_forest_S1,
subgroup = target,
studlab = Analysis)
meta::forest(meta_res_tot, random = FALSE, fixed = FALSE,
leftcols = c("studlab"),
text.subgroup.nohet = FALSE, print.tau2 = FALSE, print.I2 = FALSE, print.pval.Q = FALSE,
plotwidth = "15cm", colgap.left = "1cm", colgap.right = "1cm",
leftlabs= c(""),
just = "right", spacing = 0.8)
S3. Small study effects & ESS
Nakaraga method
dat.meta$inv_n_tilda <- with(dat.meta, (n_nexp + n_exp)/(n_nexp*n_exp))
dat.meta$sqrt_inv_n_tilda <- with(dat.meta, sqrt(inv_n_tilda))
nak.pb = metafor::rma.mv(yi = es, V = V.SCE,
data = dat.meta,
mods = ~ 1 + sqrt_inv_n_tilda + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(nak.pb, vcov = "CR2", cluster = dat.meta$ID_study)## Coef. Estimate
## 1 intrcpt 0.1052
## 2 sqrt_inv_n_tilda 5.3163
## 3 outcome_RDoCMixed social processes (non-adjusted) -0.0949
## 4 outcome_RDoCReception of facial communication (adjusted) -0.7971
## 5 outcome_RDoCReception of facial communication (non-adjusted) -0.7749
## 6 outcome_RDoCReception of non-facial communication (adjusted) -0.9621
## 7 outcome_RDoCReception of non-facial communication (non-adjusted) -0.8632
## 8 outcome_RDoCUnderstanding mental states (adjusted) -0.8543
## 9 outcome_RDoCUnderstanding mental states (non-adjusted) -0.6194
## SE t-stat d.f. p-val (Satt) Sig.
## 1 0.541 0.194 8.97 0.8502
## 2 1.969 2.700 17.39 0.0150 *
## 3 0.258 -0.368 8.56 0.7216
## 4 0.319 -2.500 10.61 0.0302 *
## 5 0.264 -2.932 9.35 0.0161 *
## 6 0.368 -2.615 2.14 0.1122
## 7 0.292 -2.953 11.74 0.0123 *
## 8 0.317 -2.697 9.79 0.0228 *
## 9 0.259 -2.393 9.53 0.0389 *PET-PEESE method (Stanley 2014)
PET = metafor::rma.mv(yi = es, V = V.SCE,
data = dat.meta,
mods = ~ se,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(PET, vcov = "CR2", cluster = dat.meta$ID_study)## Coef. Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt -2.91 0.453 -6.42 72.7 <0.001 ***
## 2 se 14.53 1.692 8.59 24.5 <0.001 ***dat.meta$var = dat.meta$se^2
PET = metafor::rma.mv(yi = es, V = V.SCE,
data = dat.meta,
mods = ~ var,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(PET, vcov = "CR2", cluster = dat.meta$ID_study)## Coef. Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 intrcpt -0.86 0.187 -4.61 75.9 <0.001 ***
## 2 var 23.47 2.454 9.56 18.9 <0.001 ***Split by outcome : Egger’s test + ESS
dat_ess = data.frame(
meta_review = "Haza",
study = dat.borenstein$ID_study,
factor = dat.borenstein$outcome_RDoC,
value = dat.borenstein$es,
se = dat.borenstein$se,
n_cases = dat.borenstein$n_exp,
n_controls = dat.borenstein$n_nexp,
measure = "G")
umb = metaumbrella::umbrella(dat_ess)## Analyzing factor: Understanding mental states (non-adjusted)
## Analyzing factor: Mixed social processes (non-adjusted)
## Analyzing factor: Mixed social processes (adjusted)
## Analyzing factor: Understanding mental states (adjusted)
## Analyzing factor: Reception of facial communication (non-adjusted)
## Analyzing factor: Reception of non-facial communication (non-adjusted)## - An error occured when converting the standard error of G to SMD. The standard error of the SMD was assumed to be equal to 'sqrt(1/n_cases + 1/n_controls)'.## Analyzing factor: Reception of facial communication (adjusted)
## Analyzing factor: Reception of non-facial communication (adjusted)umb##
## Umbrella review:
## Factor n_studies total_n
## 1 Understanding mental states (non-adjusted) 56 4917
## 2 Mixed social processes (non-adjusted) 82 10770
## 3 Mixed social processes (adjusted) 8 583
## 4 Understanding mental states (adjusted) 9 519
## 5 Reception of facial communication (non-adjusted) 42 3369
## 6 Reception of non-facial communication (non-adjusted) 8 707
## 7 Reception of facial communication (adjusted) 6 893
## 8 Reception of non-facial communication (adjusted) 2 395
## n_cases n_controls measure value value_CI eG eG_CI eOR
## 1 2521 2396 G 0.904 [0.731, 1.078] 0.904 [0.731, 1.078] 5.158
## 2 5570 5200 G 1.107 [0.957, 1.257] 1.107 [0.957, 1.257] 7.450
## 3 277 306 G 0.983 [0.336, 1.63] 0.983 [0.336, 1.63] 5.950
## 4 245 274 G 0.808 [0.495, 1.121] 0.808 [0.495, 1.121] 4.331
## 5 1712 1657 G 0.768 [0.614, 0.922] 0.768 [0.614, 0.922] 4.029
## 6 381 326 G 0.851 [0.419, 1.283] 0.851 [0.419, 1.283] 4.683
## 7 382 511 G 0.407 [0.219, 0.595] 0.407 [0.219, 0.595] 2.093
## 8 141 254 G 0.046 [-0.241, 0.333] 0.046 [-0.241, 0.333] 1.087
## eOR_CI p_value I2 PI_eG PI_eOR egger_p
## 1 [3.766, 7.064] 1.60e-24 85.229 [-0.323, 2.132] [0.557, 47.779] 1.56e-01
## 2 [5.677, 9.778] 1.60e-47 89.997 [-0.182, 2.397] [0.719, 77.243] 3.65e-04
## 3 [1.84, 19.238] 2.90e-03 91.492 [-1.361, 3.328] [0.085, 418.069] 1.16e-01
## 4 [2.455, 7.643] 4.22e-07 69.564 [-0.219, 1.835] [0.672, 27.903] 2.29e-01
## 5 [3.048, 5.325] 1.24e-22 82.455 [-0.139, 1.676] [0.777, 20.893] 1.32e-01
## 6 [2.138, 10.257] 1.14e-04 80.489 [-0.617, 2.319] [0.327, 67.13] 1.50e-04
## 7 [1.488, 2.943] 2.19e-05 48.194 [-0.11, 0.925] [0.818, 5.35] 3.69e-01
## 8 [0.646, 1.83] 7.53e-01 0.000 < 3 studies < 3 studies < 3 studies
## ESB_p power_med JK_p largest_CI_eG largest_CI_eOR rob amstar
## 1 6.51e-01 100.0 1.99e-23 [-0.017, 0.904] [0.97, 5.152] NA NA
## 2 6.85e-01 100.0 4.32e-46 [0.281, 1.239] [1.666, 9.464] NA NA
## 3 3.24e-01 100.0 1.40e-02 [0.623, 1.315] [3.095, 10.864] NA NA
## 4 7.40e-01 100.0 1.54e-05 [0.271, 0.939] [1.634, 5.494] NA NA
## 5 9.65e-01 100.0 2.80e-21 [1.259, 1.804] [9.804, 26.348] NA NA
## 6 8.16e-02 100.0 1.13e-03 [-0.12, 0.493] [0.804, 2.444] NA NA
## 7 4.96e-01 100.0 2.30e-03 [0.165, 0.879] [1.348, 4.928] NA NA
## 8 <NA> 99.7 9.74e-01 [-0.361, 0.373] [0.519, 1.969] NA NA# metaumbrella::umbrella(subset(dat_ess, factor == "Mixed social processes (non-adjusted)"))[[1]]$egger$p.value
# metafor::regtest(x = dat_sub$es, sei = dat_sub$se, model = "lm", predictor="sei")$pvalPlot
ggplot(dat.borenstein, aes(x=es, y=se)) +
geom_point(alpha = 0.2) +
geom_smooth(method="lm") +
facet_wrap(~outcome_RDoC, scales='free') +
theme_bw()## `geom_smooth()` using formula 'y ~ x'## Warning in qt((1 - level)/2, df): NaNs produced## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
S4. type of publication
SCE.S2 <- metafor::rma.mv(yi = es, V = V.SCE,
data = dat.meta,
mods = ~ type_publication - 1 + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S2, vcov = "CR2", cluster = dat.meta$ID_study)## Coef. Estimate
## 1 type_publicationArticle 1.467
## 2 type_publicationConférence 3.693
## 3 type_publicationPoster 1.945
## 4 type_publicationThèse 1.537
## 5 outcome_RDoCMixed social processes (non-adjusted) -0.284
## 6 outcome_RDoCReception of facial communication (adjusted) -1.092
## 7 outcome_RDoCReception of facial communication (non-adjusted) -0.826
## 8 outcome_RDoCReception of non-facial communication (adjusted) -1.334
## 9 outcome_RDoCReception of non-facial communication (non-adjusted) -0.995
## 10 outcome_RDoCUnderstanding mental states (adjusted) -0.733
## 11 outcome_RDoCUnderstanding mental states (non-adjusted) -0.648
## SE t-stat d.f. p-val (Satt) Sig.
## 1 0.234 6.26 7.00 < 0.001 ***
## 2 0.244 15.15 8.24 < 0.001 ***
## 3 0.617 3.15 1.67 0.10972
## 4 0.256 5.99 9.75 < 0.001 ***
## 5 0.244 -1.17 8.24 0.27612
## 6 0.237 -4.62 8.93 0.00128 **
## 7 0.246 -3.35 8.64 0.00899 **
## 8 0.232 -5.76 6.65 < 0.001 ***
## 9 0.284 -3.50 12.29 0.00420 **
## 10 0.265 -2.76 9.35 0.02118 *
## 11 0.243 -2.67 8.74 0.02634 *ggplot(dat.borenstein, aes(x = type_publication, y = es)) +
geom_point(size = 3, alpha = 0.2) +
geom_boxplot() +
theme_bw()
S5. Each facial emotion
Data analysis
dat.meta.S3 = subset(dat.meta,
emot_spec %in% c("Happy", "Sad", "Surprise", "Fear", "Disgust", "Anger") &
grepl("Reception of facial", dat.meta$outcome_RDoC, fixed = TRUE) &
!grepl("(adjusted)", dat.meta$outcome_RDoC, fixed = TRUE))
V.SCE.S3 <- with(dat.meta.S3,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = emot_spec))
# all.equal(dat.meta$se^2, diag(unlist(V.SCE)))
SCE.S3 <- metafor::rma.mv(yi = es, V = V.SCE.S3,
data = dat.meta.S3,
mods = ~ emot_spec - 1,
random = ~ emot_spec | ID_study,
struct = "DIAG",
sparse = TRUE)
SCE.ISQ.S3 <- metafor::rma.mv(yi = es, V = V.SCE.S3,
data = dat.meta.S3,
mods = ~ emot_spec - 1)
res.SCE.S3 <- data.frame(clubSandwich::coef_test(SCE.S3, vcov = "CR2", cluster = dat.meta.S3$ID_study)) # , cluster = df.ES$researchgroup
res.SCE.ci.S3 = data.frame(clubSandwich::conf_int(SCE.S3, vcov = "CR2", cluster = dat.meta.S3$ID_study))
res.SCE.S3$emot_spec <- gsub("emot_spec", "", row.names(res.SCE.S3))
res.SCE.ci.S3$emot_spec <- gsub("emot_spec", "", row.names(res.SCE.ci.S3))
res.SCE.S3 = dplyr::left_join(res.SCE.S3, res.SCE.ci.S3)## Joining, by = c("beta", "SE", "df", "emot_spec")res.SCE.S3$I2 = het(SCE.S3, SCE.ISQ.S3)
res.SCE.clean.S3 = subset(res.SCE.S3, SE > 0.000001)Table
dat_emot$emot_spec = dat_emot$nom_sousdim_clean
res_S3 <- left_join(res.SCE.clean.S3, dat_emot)## Joining, by = "emot_spec"res_S3[,c("beta", "SE", "tstat", "df", "p_Satt", "CI_L", "CI_U", "I2", "n_studies", "n_nexp_out" , "n_exp_out")] <- apply(res_S3[,c("beta", "SE", "tstat", "df", "p_Satt", "CI_L", "CI_U", "I2", "n_studies", "n_nexp_out" , "n_exp_out")], 2, function(x) round(x, 3))
DT::datatable(res_S3,
rownames = FALSE,
options = list( # options
scrollX = TRUE,
dom = c('ft'),
autoWidth = TRUE,
columnDefs = list(
list(className = 'dt-center',
targets = "_all"))))Forest plot
res_S3$raw_outcome <- gsub(r"{\s*\([^\)]+\)}","",
as.character(res_S3$nom_sousdim_clean))
tab.plot.S3 <- data.frame(
Outcome = do.call(rbind, lapply(res_S3$nom_sousdim_clean,
function(x) paste(strwrap(x, width = 60),
collapse = "\n"))),
n_ADHD = res_S3$n_exp_out,
n_nexpT = res_S3$n_nexp_out,
n_studies = res_S3$n_studies,
I2 = paste0(round(res_S3$I2), "%"))
value_num_plot_S3 = data.frame(apply(res_S3[, c("beta", "SE")], 2, function(x) as.numeric(as.character(x))))
forest_modif(x = value_num_plot_S3[ ,c("beta", "SE")],
variant = "classic",
col = "Greys", xlab = "SMD", annotate_CI = TRUE,
study_table = tab.plot.S3,
group = res_S3$raw_outcome,
type = "study_only",
text_size = 3.5,
x_limit = c(-1, 2),
N = tab.plot.S3$n_ADHD + tab.plot.S3$n_nexpT,
x_breaks = seq(-3, 3, 1)
)
S6. Effect of age
a. Age
dat.meta.S6 = subset(dat.meta, !is.na(age) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
V.SCE.S6 <- with(dat.meta.S6,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S6 <- metafor::rma.mv(yi = es, V = V.SCE.S6,
data = dat.meta.S6,
mods = ~ age * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S6, vcov = "CR2", cluster = dat.meta.S6$ID_study)## Coef. Estimate
## 1 intrcpt 1.26246
## 2 age -0.00415
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.44699
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.19454
## 5 age:outcome_RDoCReception of facial communication (non-adjusted) -0.01131
## 6 age:outcome_RDoCUnderstanding mental states (non-adjusted) -0.01733
## SE t-stat d.f. p-val (Satt) Sig.
## 1 1.0326 1.2226 17.7 0.237
## 2 0.0994 -0.0418 15.5 0.967
## 3 1.1470 -0.3897 32.4 0.699
## 4 1.3378 -0.1454 34.8 0.885
## 5 0.1105 -0.1023 26.5 0.919
## 6 0.1279 -0.1355 30.6 0.893anova(SCE.S6, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 0.1479, p-val = 0.9287SCE.S6 <- metafor::rma.mv(yi = es, V = V.SCE.S6,
data = dat.meta.S6,
mods = ~ age + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S6, vcov = "CR2", cluster = dat.meta.S6$ID_study)## Coef. Estimate SE
## 1 intrcpt 1.3479 0.4553
## 2 age -0.0125 0.0426
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.5644 0.1051
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.3769 0.1081
## t-stat d.f. p-val (Satt) Sig.
## 1 2.961 43.5 0.00495 **
## 2 -0.294 32.8 0.77064
## 3 -5.371 86.4 < 0.001 ***
## 4 -3.486 99.2 < 0.001 ***ggplot(dat.meta.S6, aes(x = age, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()
b. Age diff
dat.meta.S6 = subset(dat.meta, !is.na(age_diff) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
V.SCE.S6 <- with(dat.meta.S6,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S6 <- metafor::rma.mv(yi = es, V = V.SCE.S6,
data = dat.meta.S6,
mods = ~ age_diff * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S6, vcov = "CR2", cluster = dat.meta.S6$ID_study)## Coef.
## 1 intrcpt
## 2 age_diff
## 3 outcome_RDoCReception of facial communication (non-adjusted)
## 4 outcome_RDoCUnderstanding mental states (non-adjusted)
## 5 age_diff:outcome_RDoCReception of facial communication (non-adjusted)
## 6 age_diff:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 1.21622 0.0785 15.4931 79.23 <0.001 ***
## 2 -0.05515 0.2292 -0.2406 3.44 0.823
## 3 -0.55690 0.1106 -5.0361 87.49 <0.001 ***
## 4 -0.38005 0.1107 -3.4346 100.83 <0.001 ***
## 5 -0.04965 0.2488 -0.1996 5.46 0.849
## 6 0.00274 0.2589 0.0106 5.34 0.992anova(SCE.S6, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 1.4237, p-val = 0.4907SCE.S6 <- metafor::rma.mv(yi = es, V = V.SCE.S6,
data = dat.meta.S6,
mods = ~ age_diff + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S6, vcov = "CR2", cluster = dat.meta.S6$ID_study)## Coef. Estimate SE
## 1 intrcpt 1.216 0.0761
## 2 age_diff -0.066 0.0601
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.561 0.1083
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.379 0.1096
## t-stat d.f. p-val (Satt) Sig.
## 1 15.98 79.29 <0.001 ***
## 2 -1.10 6.23 0.313
## 3 -5.18 88.49 <0.001 ***
## 4 -3.46 100.82 <0.001 ***ggplot(dat.meta.S6, aes(x = age_diff, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth() +
theme_bw()
S7. Effect of comorbidities
a. ASD
dat.meta.S7_a = subset(dat.meta, !is.na(percentage_asd) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]) & percentage_asd %in% c(0,100))
dat.meta.S7_a$percentage_asd = factor(dat.meta.S7_a$percentage_asd)
V.SCE.S7_a <- with(dat.meta.S7_a,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S7_a <- metafor::rma.mv(yi = es, V = V.SCE.S7_a,
data = dat.meta.S7_a,
mods = ~ percentage_asd * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S7_a, vcov = "CR2", cluster = dat.meta.S7_a$ID_study)## Coef.
## 1 intrcpt
## 2 percentage_asd100
## 3 outcome_RDoCReception of facial communication (non-adjusted)
## 4 outcome_RDoCUnderstanding mental states (non-adjusted)
## 5 percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted)
## 6 percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 1.333 0.0961 13.88 45.40 < 0.001 ***
## 2 1.059 0.2195 4.82 4.62 0.00588 **
## 3 -0.765 0.1375 -5.57 50.26 < 0.001 ***
## 4 -0.524 0.1413 -3.71 55.82 < 0.001 ***
## 5 -0.979 0.2187 -4.48 2.62 0.02762 *
## 6 -0.752 0.2507 -3.00 3.48 0.04775 *anova(SCE.S7_a, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 187.9798, p-val < .0001SCE.S7_a_ph = metafor::rma.mv(yi = es, V = V.SCE.S7_a,
data = dat.meta.S7_a,
mods = ~ percentage_asd : outcome_RDoC - 1,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
SCE.S7_a_ph##
## Multivariate Meta-Analysis Model (k = 376; method: REML)
##
## Variance Components:
##
## outer factor: ID_study (nlvls = 76)
## inner factor: outcome_RDoC (nlvls = 3)
##
## estim sqrt k.lvl fixed
## tau^2.1 0.3634 0.6028 120 no
## tau^2.2 0.2202 0.4693 131 no
## tau^2.3 0.2980 0.5459 125 no
## level
## tau^2.1 Mixed social processes (non-adjusted)
## tau^2.2 Reception of facial communication (non-adjusted)
## tau^2.3 Understanding mental states (non-adjusted)
##
## Test for Residual Heterogeneity:
## QE(df = 370) = 6343.8250, p-val < .0001
##
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 801.6536, p-val < .0001
##
## Model Results:
##
## estimate
## percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) 1.3334
## percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) 2.3923
## percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) 0.5682
## percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) 0.6484
## percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) 0.8093
## percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) 1.1162
## se
## percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) 0.0963
## percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) 0.1062
## percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) 0.1043
## percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) 0.1190
## percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) 0.1046
## percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) 0.1158
## zval
## percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) 13.8460
## percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) 22.5282
## percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) 5.4476
## percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) 5.4502
## percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) 7.7362
## percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) 9.6390
## pval
## percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) <.0001
## percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) <.0001
## percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) <.0001
## percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) <.0001
## percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) <.0001
## percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) <.0001
## ci.lb
## percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) 1.1447
## percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) 2.1842
## percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) 0.3638
## percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) 0.4152
## percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) 0.6043
## percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) 0.8892
## ci.ub
## percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) 1.5222
## percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) 2.6005
## percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) 0.7727
## percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) 0.8816
## percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) 1.0144
## percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) 1.3432
##
## percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) ***
## percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) ***
## percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) ***
## percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) ***
## percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) ***
## percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(SCE.S7_a_ph, L=c(-1,1,0,0,0,0))##
## Hypothesis:
## 1: -percentage_asd0:outcome_RDoCMixed social processes (non-adjusted) + percentage_asd100:outcome_RDoCMixed social processes (non-adjusted) = 0
##
## Results:
## estimate se zval pval
## 1: 1.0589 0.0499 21.2047 <.0001
##
## Test of Hypothesis:
## QM(df = 1) = 449.6405, p-val < .0001anova(SCE.S7_a_ph, L=c(0,0,-1,1,0,0))##
## Hypothesis:
## 1: -percentage_asd0:outcome_RDoCReception of facial communication (non-adjusted) + percentage_asd100:outcome_RDoCReception of facial communication (non-adjusted) = 0
##
## Results:
## estimate se zval pval
## 1: 0.0801 0.0594 1.3495 0.1772
##
## Test of Hypothesis:
## QM(df = 1) = 1.8212, p-val = 0.1772anova(SCE.S7_a_ph, L=c(0,0,0,0,-1,1))##
## Hypothesis:
## 1: -percentage_asd0:outcome_RDoCUnderstanding mental states (non-adjusted) + percentage_asd100:outcome_RDoCUnderstanding mental states (non-adjusted) = 0
##
## Results:
## estimate se zval pval
## 1: 0.3069 0.0520 5.9049 <.0001
##
## Test of Hypothesis:
## QM(df = 1) = 34.8673, p-val < .0001ggplot(dat.meta.S7_a, aes(x = percentage_asd, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_boxplot(alpha = 0.2) +
theme_bw()
b. ODD/CD
dat.meta.S7_b = subset(dat.meta, !is.na(percentage_cd_odd) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
dat.meta.S7_b$percentage_cd_odd = as.numeric(as.character(dat.meta.S7_b$percentage_cd_odd))
V.SCE.S7_b <- with(dat.meta.S7_b,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S7_b <- metafor::rma.mv(yi = es, V = V.SCE.S7_b,
data = dat.meta.S7_b,
mods = ~ percentage_cd_odd * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S7_b, vcov = "CR2", cluster = dat.meta.S7_b$ID_study)## Coef.
## 1 intrcpt
## 2 percentage_cd_odd
## 3 outcome_RDoCReception of facial communication (non-adjusted)
## 4 outcome_RDoCUnderstanding mental states (non-adjusted)
## 5 percentage_cd_odd:outcome_RDoCReception of facial communication (non-adjusted)
## 6 percentage_cd_odd:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 1.24897 0.12779 9.77 35.43 < 0.001 ***
## 2 0.00381 0.00237 1.61 1.20 0.32339
## 3 -0.57724 0.16979 -3.40 50.73 0.00132 **
## 4 -0.38735 0.16563 -2.34 50.38 0.02336 *
## 5 -0.00349 0.00283 -1.23 1.74 0.35823
## 6 -0.00473 0.00256 -1.85 1.68 0.22971anova(SCE.S7_b, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 18.3078, p-val = 0.0001SCE.S7_b_ph = metafor::rma.mv(yi = es, V = V.SCE.S7_b,
data = dat.meta.S7_b,
mods = ~ percentage_cd_odd : outcome_RDoC - 1,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S7_b_ph, vcov = "CR2", cluster = dat.meta.S7_b$ID_study)## Coef.
## 1 percentage_cd_odd:outcome_RDoCMixed social processes (non-adjusted)
## 2 percentage_cd_odd:outcome_RDoCReception of facial communication (non-adjusted)
## 3 percentage_cd_odd:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 0.004147 0.00196 2.112 1.14 0.257
## 2 0.000432 0.00153 0.282 2.25 0.801
## 3 -0.000504 0.00102 -0.496 1.27 0.692ggplot(dat.meta.S7_b, aes(x = percentage_cd_odd, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()
S8. Effect of sex
dat.meta.S8 = subset(dat.meta, !is.na(sex) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
dat.meta.S8$sex = as.numeric(as.character(dat.meta.S8$sex))
dat.meta.S8$sex[dat.meta.S8$sex > 100] <- NA
V.SCE.S8 <- with(dat.meta.S8,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S8 <- metafor::rma.mv(yi = es, V = V.SCE.S8,
data = dat.meta.S8,
mods = ~ sex * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S8, vcov = "CR2", cluster = dat.meta.S8$ID_study)## Coef. Estimate
## 1 intrcpt 1.44135
## 2 sex -0.00777
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.86490
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.59449
## 5 sex:outcome_RDoCReception of facial communication (non-adjusted) 0.00958
## 6 sex:outcome_RDoCUnderstanding mental states (non-adjusted) 0.00691
## SE t-stat d.f. p-val (Satt) Sig.
## 1 0.18176 7.930 26.89 < 0.001 ***
## 2 0.00627 -1.240 5.45 0.26564
## 3 0.26479 -3.266 22.45 0.00347 **
## 4 0.23033 -2.581 28.37 0.01530 *
## 5 0.00971 0.986 10.41 0.34637
## 6 0.00720 0.960 10.33 0.35881anova(SCE.S8, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 5.7438, p-val = 0.0566SCE.S8 <- metafor::rma.mv(yi = es, V = V.SCE.S8,
data = dat.meta.S8,
mods = ~ sex + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S8, vcov = "CR2", cluster = dat.meta.S8$ID_study)## Coef. Estimate SE
## 1 intrcpt 1.35953 0.13810
## 2 sex -0.00486 0.00453
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.60969 0.11629
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.41569 0.11203
## t-stat d.f. p-val (Satt) Sig.
## 1 9.84 53.5 <0.001 ***
## 2 -1.07 10.9 0.306
## 3 -5.24 70.8 <0.001 ***
## 4 -3.71 89.2 <0.001 ***ggplot(dat.meta.S8, aes(x = sex, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()
S9. Effect of IQ
a. IQ
dat.meta.S9 = subset(dat.meta, !is.na(iq) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
dat.meta.S9$iq = as.numeric(as.character(dat.meta.S9$iq))
# dat.meta.S9$iq[dat.meta.S9$iq > 100] <- NA
V.SCE.S9 <- with(dat.meta.S9,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S9 <- metafor::rma.mv(yi = es, V = V.SCE.S9,
data = dat.meta.S9,
mods = ~ iq * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S9, vcov = "CR2", cluster = dat.meta.S9$ID_study)## Coef. Estimate
## 1 intrcpt -0.904696
## 2 iq 0.020699
## 3 outcome_RDoCReception of facial communication (non-adjusted) 2.137153
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.646296
## 5 iq:outcome_RDoCReception of facial communication (non-adjusted) -0.026903
## 6 iq:outcome_RDoCUnderstanding mental states (non-adjusted) 0.000924
## SE t-stat d.f. p-val (Satt) Sig.
## 1 7.2554 -0.1247 5.69 0.905
## 2 0.0697 0.2969 5.82 0.777
## 3 7.5143 0.2844 8.29 0.783
## 4 9.2993 -0.0695 5.41 0.947
## 5 0.0722 -0.3728 8.34 0.719
## 6 0.0892 0.0104 5.28 0.992anova(SCE.S9, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 2.2610, p-val = 0.3229SCE.S9 <- metafor::rma.mv(yi = es, V = V.SCE.S9,
data = dat.meta.S9,
mods = ~ iq + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S9, vcov = "CR2", cluster = dat.meta.S9$ID_study)## Coef. Estimate SE
## 1 intrcpt -0.3227 3.9968
## 2 iq 0.0151 0.0385
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.6526 0.1537
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.5448 0.1387
## t-stat d.f. p-val (Satt) Sig.
## 1 -0.0807 9.77 0.937
## 2 0.3924 9.79 0.703
## 3 -4.2470 43.58 <0.001 ***
## 4 -3.9290 45.10 <0.001 ***ggplot(dat.meta.S9, aes(x = iq, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()
b. IQ diff
dat.meta.S9 = subset(dat.meta, !is.na(iq_diff) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
dat.meta.S9$iq_diff = as.numeric(as.character(dat.meta.S9$iq_diff))
SCE.S9 <- metafor::rma.mv(yi = es, V = V.SCE.S9,
data = dat.meta.S9,
mods = ~ iq_diff * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S9, vcov = "CR2", cluster = dat.meta.S9$ID_study)## Coef.
## 1 intrcpt
## 2 iq_diff
## 3 outcome_RDoCReception of facial communication (non-adjusted)
## 4 outcome_RDoCUnderstanding mental states (non-adjusted)
## 5 iq_diff:outcome_RDoCReception of facial communication (non-adjusted)
## 6 iq_diff:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 1.363368 0.4047 3.36917 29.97 0.00209 **
## 2 0.013140 0.0441 0.29812 5.39 0.77676
## 3 -0.790680 0.4146 -1.90726 37.65 0.06414 .
## 4 -0.554209 0.4759 -1.16447 33.85 0.25238
## 5 -0.017017 0.0468 -0.36396 3.35 0.73768
## 6 0.000401 0.0545 0.00737 5.85 0.99437anova(SCE.S9, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 2.2257, p-val = 0.3286SCE.S9 <- metafor::rma.mv(yi = es, V = V.SCE.S9,
data = dat.meta.S9,
mods = ~ iq_diff + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S9, vcov = "CR2", cluster = dat.meta.S9$ID_study)## Coef. Estimate SE
## 1 intrcpt 1.3423 0.2553
## 2 iq_diff 0.0107 0.0246
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.7065 0.1910
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.5525 0.1400
## t-stat d.f. p-val (Satt) Sig.
## 1 5.257 33.28 <0.001 ***
## 2 0.436 8.51 0.674
## 3 -3.698 43.75 <0.001 ***
## 4 -3.948 44.93 <0.001 ***ggplot(dat.meta.S9, aes(x = iq_diff, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()
S10. Effect of rob
dat.meta.S10 = subset(dat.meta, !is.na(rob_tot) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
V.SCE.S10 <- with(dat.meta.S10,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S10 <- metafor::rma.mv(yi = es, V = V.SCE.S10,
data = dat.meta.S10,
mods = ~ rob_tot * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S10, vcov = "CR2", cluster = dat.meta.S10$ID_study)## Coef. Estimate
## 1 intrcpt 1.13282
## 2 rob_tot 0.01513
## 3 outcome_RDoCReception of facial communication (non-adjusted) -1.07372
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.43890
## 5 rob_tot:outcome_RDoCReception of facial communication (non-adjusted) 0.07767
## 6 rob_tot:outcome_RDoCUnderstanding mental states (non-adjusted) 0.00716
## SE t-stat d.f. p-val (Satt) Sig.
## 1 0.2725 4.156 5.60 0.00695 **
## 2 0.0474 0.319 4.05 0.76560
## 3 0.4356 -2.465 21.11 0.02237 *
## 4 0.2842 -1.544 5.00 0.18319
## 5 0.0739 1.051 19.70 0.30594
## 6 0.0486 0.147 4.14 0.88977anova(SCE.S10, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 1.6539, p-val = 0.4374SCE.S10 <- metafor::rma.mv(yi = es, V = V.SCE.S10,
data = dat.meta.S10,
mods = ~ rob_tot + outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S10, vcov = "CR2", cluster = dat.meta.S10$ID_study)## Coef. Estimate SE
## 1 intrcpt 1.0640 0.1740
## 2 rob_tot 0.0269 0.0295
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.5868 0.1066
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.3994 0.1100
## t-stat d.f. p-val (Satt) Sig.
## 1 6.12 9.71 <0.001 ***
## 2 0.91 5.62 0.4
## 3 -5.50 88.14 <0.001 ***
## 4 -3.63 98.02 <0.001 ***ggplot(dat.meta.S10, aes(x = rob_tot, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth() + #method ="lm"
theme_bw()
S11. Type of tools
dat.meta.S11 = subset(dat.meta, !is.na(type_mesure) & outcome_RDoC== "Understanding mental states (non-adjusted)")
dat.meta.S11$vi = dat.meta.S11$se^2
dat.meta.S11$es_id = 1:nrow(dat.meta.S11)
SCE.S11 <- metafor::rma.mv(yi = es, V = vi,
data = dat.meta.S11,
mods = ~ type_mesure ,
random = ~ 1 | ID_study/es_id)
metafor::rma.mv(yi = es, V = vi,
data = dat.meta.S11,
mods = ~ type_mesure - 1,
random = ~ 1 | ID_study/es_id)##
## Multivariate Meta-Analysis Model (k = 186; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2319 0.4815 56 no ID_study
## sigma^2.2 0.1638 0.4048 186 no ID_study/es_id
##
## Test for Residual Heterogeneity:
## QE(df = 184) = 1025.9014, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## QM(df = 2) = 112.2712, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## type_mesureParadigm 0.7539 0.0819 9.2028 <.0001 0.5934 0.9145 ***
## type_mesureReport 1.0250 0.1322 7.7540 <.0001 0.7659 1.2841 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(dat.meta.S11, aes(x = type_mesure, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_boxplot(alpha = 0.2) +
geom_smooth() + #method ="lm"
theme_bw()
S12. ADHD presentation
a. Inattentive
dat.meta.S12.a = subset(dat.meta, !is.na(percentage_inattentive) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
dat.meta.S12.a$percentage_inattentive = as.numeric(as.character(dat.meta.S12.a$percentage_inattentive))
V.SCE.S12.a <- with(dat.meta.S12.a,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S12.a <- metafor::rma.mv(yi = es, V = V.SCE.S12.a,
data = dat.meta.S12.a,
mods = ~ percentage_inattentive * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S12.a, vcov = "CR2", cluster = dat.meta.S12.a$ID_study)## Coef.
## 1 intrcpt
## 2 percentage_inattentive
## 3 outcome_RDoCReception of facial communication (non-adjusted)
## 4 outcome_RDoCUnderstanding mental states (non-adjusted)
## 5 percentage_inattentive:outcome_RDoCReception of facial communication (non-adjusted)
## 6 percentage_inattentive:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 1.33e+00 0.113388 11.6973 33.72 < 0.001 ***
## 2 -2.86e-03 0.000694 -4.1235 1.13 0.12849
## 3 -5.50e-01 0.204845 -2.6863 14.61 0.01721 *
## 4 -4.72e-01 0.168141 -2.8044 41.71 0.00762 **
## 5 -7.51e-05 0.005913 -0.0127 8.43 0.99017
## 6 3.66e-03 0.000899 4.0764 1.12 0.13271anova(SCE.S12.a, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 33.4410, p-val < .0001SCE.S12.a <- metafor::rma.mv(yi = es, V = V.SCE.S12.a,
data = dat.meta.S12.a,
mods = ~ percentage_inattentive:outcome_RDoC - 1,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S12.a, vcov = "CR2", cluster = dat.meta.S12.a$ID_study)## Coef.
## 1 percentage_inattentive:outcome_RDoCMixed social processes (non-adjusted)
## 2 percentage_inattentive:outcome_RDoCReception of facial communication (non-adjusted)
## 3 percentage_inattentive:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 -0.002799 0.000795 -3.52 1.13 0.1538
## 2 0.013239 0.005376 2.46 7.34 0.0418 *
## 3 0.000975 0.000331 2.95 1.05 0.1992ggplot(dat.meta.S12.a, aes(x = percentage_inattentive, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()
b. Hyperactive
dat.meta.S12.b = subset(dat.meta, !is.na(percentage_hyperactive) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
dat.meta.S12.b$percentage_hyperactive = as.numeric(as.character(dat.meta.S12.b$percentage_hyperactive))
V.SCE.S12.b <- with(dat.meta.S12.b,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S12.b <- metafor::rma.mv(yi = es, V = V.SCE.S12.b,
data = dat.meta.S12.b,
mods = ~ percentage_hyperactive * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S12.b, vcov = "CR2", cluster = dat.meta.S12.b$ID_study)## Coef.
## 1 intrcpt
## 2 percentage_hyperactive
## 3 outcome_RDoCReception of facial communication (non-adjusted)
## 4 outcome_RDoCUnderstanding mental states (non-adjusted)
## 5 percentage_hyperactive:outcome_RDoCReception of facial communication (non-adjusted)
## 6 percentage_hyperactive:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 1.260353 0.1580 7.978 32.46 <0.001 ***
## 2 0.000807 0.0106 0.076 1.16 0.9504
## 3 -0.606438 0.2032 -2.984 35.80 0.0051 **
## 4 -0.432498 0.2143 -2.018 40.58 0.0502 .
## 5 0.003804 0.0120 0.317 1.61 0.7878
## 6 0.006235 0.0195 0.320 1.94 0.7798anova(SCE.S12.b, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 2.6712, p-val = 0.2630SCE.S12.b <- metafor::rma.mv(yi = es, V = V.SCE.S12.b,
data = dat.meta.S12.b,
mods = ~ percentage_hyperactive+outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S12.b, vcov = "CR2", cluster = dat.meta.S12.b$ID_study)## Coef. Estimate SE
## 1 intrcpt 1.25097 0.13739
## 2 percentage_hyperactive 0.00177 0.00721
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.57155 0.16800
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.38331 0.16787
## t-stat d.f. p-val (Satt) Sig.
## 1 9.105 32.74 < 0.001 ***
## 2 0.246 1.52 0.83500
## 3 -3.402 39.13 0.00155 **
## 4 -2.283 42.44 0.02748 *ggplot(dat.meta.S12.b, aes(x = percentage_hyperactive, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()
c. Combined
dat.meta.S12.c = subset(dat.meta, !is.na(percentage_combined) & outcome_RDoC %in% unique(synth_n$outcome_RDoC[synth_n$nstudy1 > 20]))
dat.meta.S12.c$percentage_combined = as.numeric(as.character(dat.meta.S12.c$percentage_combined))
V.SCE.S12.c <- with(dat.meta.S12.c,
clubSandwich::impute_covariance_matrix(
vi = se^2,
cluster = ID_study,
r = 0.8,
return_list = FALSE,
smooth_vi = TRUE,
subgroup = outcome_RDoC))
SCE.S12.c <- metafor::rma.mv(yi = es, V = V.SCE.S12.c,
data = dat.meta.S12.c,
mods = ~ percentage_combined * outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S12.c, vcov = "CR2", cluster = dat.meta.S12.c$ID_study)## Coef.
## 1 intrcpt
## 2 percentage_combined
## 3 outcome_RDoCReception of facial communication (non-adjusted)
## 4 outcome_RDoCUnderstanding mental states (non-adjusted)
## 5 percentage_combined:outcome_RDoCReception of facial communication (non-adjusted)
## 6 percentage_combined:outcome_RDoCUnderstanding mental states (non-adjusted)
## Estimate SE t-stat d.f. p-val (Satt) Sig.
## 1 1.05997 0.129152 8.207 31.17 <0.001 ***
## 2 0.00298 0.000578 5.154 1.06 0.1110
## 3 -0.26144 0.339580 -0.770 7.55 0.4648
## 4 -0.12509 0.182362 -0.686 37.05 0.4970
## 5 -0.00462 0.004235 -1.090 7.46 0.3097
## 6 -0.00389 0.000623 -6.232 1.12 0.0831 .anova(SCE.S12.c, btt=5:6)##
## Test of Moderators (coefficients 5:6):
## QM(df = 2) = 38.0168, p-val < .0001SCE.S12.c <- metafor::rma.mv(yi = es, V = V.SCE.S12.c,
data = dat.meta.S12.c,
mods = ~ percentage_combined+outcome_RDoC,
random = ~ outcome_RDoC | ID_study,
struct = "DIAG",
sparse = TRUE)
clubSandwich::coef_test(SCE.S12.c, vcov = "CR2", cluster = dat.meta.S12.c$ID_study)## Coef. Estimate
## 1 intrcpt 1.15551
## 2 percentage_combined 0.00153
## 3 outcome_RDoCReception of facial communication (non-adjusted) -0.55775
## 4 outcome_RDoCUnderstanding mental states (non-adjusted) -0.36555
## SE t-stat d.f. p-val (Satt) Sig.
## 1 0.123538 9.35 32.70 < 0.001 ***
## 2 0.000438 3.50 1.13 0.15445
## 3 0.166051 -3.36 39.26 0.00175 **
## 4 0.169476 -2.16 42.64 0.03669 *ggplot(dat.meta.S12.c, aes(x = percentage_combined, y = es)) +
facet_wrap(~outcome_RDoC) +
geom_point(size = 2, alpha = 0.2) +
geom_smooth(method ="lm") +
theme_bw()